Histogram analysis of en face scattering coefficient map predicts malignancy in human ovarian tissue.
Histogram analysis of en face scattering coefficient map predicts malignancy in human ovarian tissue.
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DOI：
10.1002/jbio.201900115
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发表时间：
2019-11
影响因子：
2.8
通讯作者：
Zhu Q
中科院分区：
文献类型：
--
作者：
Zeng Y;Nandy S;Rao B;Li S;Hagemann AR;Kuroki LK;McCourt C;Mutch DG;Powell MA;Hagemann IS;Zhu Q
卵巢癌在分子和组织学水平上是一种异质性疾病。光学相干断层扫描（OCT）能够映射卵巢组织的光学特性和异质性，这已被提出作为一个功能，以帮助诊断卵巢癌。在这篇手稿中，提供了从20例患者中获得的恶性卵巢、良性卵巢和良性输卵管的深度分辨正面散射图，以可视化卵巢组织的异质性。从散射图的直方图中提取六个特征。所有特征都能够在统计学上区分良性卵巢和恶性卵巢。基于这些特征构建了两种预测模型：逻辑回归模型（LR）和支持向量机（SVM）。最佳特征集是平均散射系数和散射图熵。LR的敏感性和特异性分别为97.0%和97.8%，SVM的敏感性和特异性分别为99.6%和96.4%。我们的初步结果表明，使用OCT作为“光学活检工具”的可行性，用于检测与人类卵巢组织中的肿瘤相关的微观散射变化。
Ovarian cancer is a heterogeneous disease at the molecular and histologic level. Optical coherence tomography (OCT) is able to map ovarian tissue optical properties and heterogeneity, which has been proposed as a feature to aid in diagnosis of ovarian cancer. In this manuscript, depth-resolved en face scattering maps of malignant ovaries, benign ovaries, and benign fallopian tubes obtained from 20 patients are provided to visualize the heterogeneity of ovarian tissues. Six features are extracted from histograms of scattering maps. All features are able to statistically distinguish benign from malignant ovaries. Two prediction models were constructed based on these features: a logistic regression model (LR) and a support vector machine (SVM). The optimal set of features is mean scattering coefficient and scattering map entropy. The LR achieved a sensitivity and specificity of 97.0% and 97.8%, and SVM demonstrated a sensitivity and specificity of 99.6% and 96.4%. Our initial results demonstrate the feasibility of using OCT as an “optical biopsy tool” for detecting the microscopic scattering changes associated with neoplasia in human ovarian tissue.
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DOI：
10.1097/pas.0b013e3181cf3d79
发表时间：
2010-03
期刊：
The American journal of surgical pathology
影响因子：
--
作者：
Kurman RJ;Shih IeM
通讯作者：
Shih IeM
影响因子：
7.2
作者：
Parker WH;Broder MS;Chang E;Feskanich D;Farquhar C;Liu Z;Shoupe D;Berek JS;Hankinson S;Manson JE
通讯作者：
Manson JE
影响因子：
50.5
作者：
Prat, J.
通讯作者：
Prat, J.
影响因子：
254.7
作者：
Jemal, Ahmedin;Siegel, Rebecca;Ward, Elizabeth
通讯作者：
Ward, Elizabeth
影响因子：
3.5
作者：
Madore, Wendy-Julie;De Montigny, Etienne;Godbout, Nicolas
通讯作者：
Godbout, Nicolas